{ "_id": "6ad1f1780edca5be25022a23", "Package": "MeLSI", "Type": "Package", "Title": "Metric Learning for Statistical Inference in Microbiome Analysis", "Version": "1.1.7", "Authors@R": "c(person(\"Nathan\", \"Bresette\", email = \"nathanbresette04@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0009-0003-1554-6006\")), person(\"Aaron C.\", \"Ericsson\", role = \"aut\", comment = c(ORCID = \"0000-0002-3053-7269\")), person(\"Carter\", \"Woods\", role = \"aut\", comment = c(ORCID = \"0009-0007-5345-2712\")), person(\"Ai-Ling\", \"Lin\", role = c(\"aut\", \"fnd\"), comment = c(ORCID = \"0000-0002-5197-2219\")))", "Description": "MeLSI (Metric Learning for Statistical Inference) is a\nnovel machine learning method for microbiome data analysis that\nlearns optimal distance metrics to improve statistical power in\ndetecting group differences. Unlike traditional distance\nmetrics (Bray-Curtis, Euclidean, Jaccard), MeLSI adapts to the\nspecific characteristics of your dataset to maximize separation\nbetween groups. The method uses an ensemble of weak learners to\nidentify which microbial features drive group differences,\nproviding both improved statistical power and biological\ninterpretability through feature importance weights.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "URL": "https://github.com/NathanBresette/MeLSI", "BugReports": "https://github.com/NathanBresette/MeLSI/issues", "VignetteBuilder": "knitr", "biocViews": "Software, StatisticalMethod, Microbiome", "Repository": "https://bioc.r-universe.dev", "Date/Publication": "2026-06-17 21:23:49 UTC", "RemoteUrl": "https://github.com/bioc/MeLSI", "RemoteRef": "HEAD", "RemoteSha": "19902ee27eb55b11d471d7afb62c7e53eccba723", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-10-16 08:21:33 UTC", "User": "root" }, "Author": "Nathan Bresette [aut, cre] (ORCID:\n),\nAaron C. Ericsson [aut] (ORCID:\n),\nCarter Woods [aut] (ORCID: ),\nAi-Ling Lin [aut, fnd] (ORCID: )", "Maintainer": "Nathan Bresette ", "_user": "bioc", "_type": "src", "_file": "MeLSI_1.1.7.tar.gz", "_fileid": "https://r2.ropensci.org/d338fca29c7e5e9fc58ab7c23426547a1f7e6a465f52727e0a23ee47417138a5", "_filesize": 154912, "_sha256": "d338fca29c7e5e9fc58ab7c23426547a1f7e6a465f52727e0a23ee47417138a5", "_expires": "2027-01-24T09:42:14.000Z", "_created": "2026-10-16T08:21:33.000Z", "_published": "2026-10-16T09:42:16.498Z", "_buildurl": "https://github.com/r-universe/bioc/actions/runs/38872670373", "_status": "success", "_host": "GitHub-Actions", "_bioccheck": { "error": 0, "warning": 2, "note": 5 }, "_upstream": "https://github.com/bioc/MeLSI", "_commit": { "id": "19902ee27eb55b11d471d7afb62c7e53eccba723", "author": "Nathan Bresette ", "committer": "Nathan Bresette ", "message": "MeLSI 1.1.7: C++ weak-learner optimizer (~4.6x, ~2x end-to-end)\n\nPort optimize_weak_learner_robust to a C++ kernel (melsi_opt_weak_learner).\nThe gradient loop draws its within/between-class pairs with R's own\nR_unif_index, so it consumes the global RNG stream identically to the\nsample()-based R loop. End-to-end F-statistics reproduce 1.1.6 to ~1e-15\nwith no p-value changes. 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